1D CNNs and face-based random walks: A powerful combination to enhance mesh understanding and 3D semantic segmentation

In this paper, we present a novel face-based random walk method aimed at addressing the 3D semantic segmentation issue. Our method utilizes a one-dimensional convolutional neural network for detailed feature extraction from sequences of triangular faces and employs a stacked gated recurrent unit to gather information along the sequence during training. This approach allows us to effectively handle irregular meshes and utilize the inherent feature extraction potential present in mesh geometry. Our study's results show that the proposed method achieves competitive results compared to the state-of-the-art methods in mesh segmentation. Importantly, it requires fewer training iterations and demonstrates versatility by applying to a wide range of objects without the need for the mesh to adhere to manifold or watertight topology requirements. •Segment 3D meshes using a hybrid architecture, gated recurrent units, and 1D convolutional neural networks.•Extraction of geometric features from 3D mesh faces using mathematical algorithms; random walks.•Apply deep learning algorithms to irregular 3D data without considering whether each mesh is a manifold or closed.